Gilbert + Tobin · Corporate AI governance and scalability

B Evidence level

Gilbert + Tobin: Corporate AI governance and scale

Publisher: OpenAI · Vendors ' official customer information · Summary page or multiple case reports

Claim origin: Public disclosure by manufacturer or customer · Independent verification: No · Accessed: 2026-09-19

Evidence level measures whether a source can be located and reviewed; it does not mean vendor-reported claims were independently audited.

Standard case | Useful reference with remaining gaps 7 / 12

Business context1 / 2

Transformation workflow1 / 2

Technical workflow2 / 2

Human roles & governance / 2

Measured outcomes2 / 2

Source traceability1 / 2

Remaining gaps: Business context, Transformation workflow, Human roles & governance, Source traceability

The key to this case is not the single-point use of AI, but the weighting of “law firm AI governance and scalability” into an enforceable, verifiable production stream with boundaries of human responsibility.

Business problem

While the legal profession wants to expand the use of AI, it also requires strict governance, confidentiality and professional responsibility.

Solution

Establish a governance and scale-up framework with OpenAI to apply AI to legal knowledge work.

Technical architecture & production workflow

Step 01

SOP / History Case / Expert Interview

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Step 02

Document Parsing, Cutting and Metadata Tags

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Step 03

Embedding / semantic index

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Step 04

Retrieving similar cases and rules based on current questions

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Step 05

LLM is evidence-based advice and attachment

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Step 06

• Rewrite the knowledge base of the new experience

Key technology & infrastructure components

Knowledge base/RAGEmbedding/vector searchPermissions and metadataLLMExpert review

Human roles & accountability

Lawyers retain professional judgement and ultimate responsibility.

FDE delivery actions

"When will the staff come to see the teacher?"
Interviews with experts to convert tacit judgment into searchable cases and rules
Design knowledge particles, labels, versions and privileges instead of simply uploading documents
Recall rate/Application of answers using real questions
Keep feeding back feedback, wrong answers and new cases.

Reusable delivery patterns

HF, high-cost, verifiable narrow-flow selection
Steps that can be validated with a certainty tool to prevent model self-assessment
Retain manual responsibility for high-risk actions
For each manual amendment to follow-up searchable context/rules

Business outcomes & delivery results

In September 2026, the case was made public; unapproved values were not added to the first edition.

Evidence boundaries & verification notes

Public information confirming business processes; technical components are abstract architecture based on public description
The current link is the official aggregation entrance, and a deep link or page number that directly locates the case has yet to be added.

Primary source: Gilbert + Tobin: Corporate AI governance and scale ↗